# library preparations
import scipy.io
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
import joblib
import seaborn as sns
import time
import os

# export PATH=/usr/local/texlive/2021/bin/x86_64-linux/xelatex:$PATH
# export PATH=/usr/local/texlive/2021/bin/x86_64-linux:$PATH
# jupyter nbconvert RandomForest.ipynb --to pdf
print(os.environ['PATH'])

/home/samsoup/anaconda3/bin:/home/samsoup/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin

Model Fitting

Given that we could just ask the simulation for more samples, I decided to NOT employ a typical training-split. Ratther, I will deploy all samples for training and optimize based on cross validation approaches, given every test record a turn at being in the training set and test set. Once the model is fitted, we can then ask the simulation for some more samples (say 1000) to use as a test set completely related here.

Model Evaluations

Many metrics can be used to evaluate models, some I calculate here are:

1. Accuracy: TP + TN / total, this is the number of samples the RF model classifiers correctly
2. Error Rate: 1 - Accuracy, this is the number of samples the RF model classifiers incorrectly
3. True Positive Rate (TPR) | Sensitivity | Recall | Coverage: TP / (TP + FN), fraction of SAS examples correctly predicted
   • There's a typically a trade off between Recall and Precision below
4. True Negative Rate (TNR) | Specificity: TN / (FP + TN), fraction of neutral examples correctly predicted
5. False Positive Rate (FPR): FP / (FP + TN) fraction of neutral examples predicted as having SAS (really bad)
6. False Negative Rate (FNR): FN / (TP + FN) fraction of SAS examples predicted as having SAS (not as bad, but still bad)
7. Precision: TP / (TP + FP), fraction of samples that actually have SAS out of total samples predicted to have SAS
   • Precision addresses the question: "Given a sample predicted to have SAS, how likely is it to be correct?"
   • We may want to sacrifice Recall in order to archieve a high precision
8. F-measure: $\frac{2*precision*recall}{precision+recall}$, a harmonic mean (meaning the resulting metric is *closer to the smaller of the input, that is, F-measure is closer to either precision or recall, whichever is smaller in magnitude) of precision and recall
   • Ideally, a F-measure should be high and indicate that both precision and recall are high
9. Area under the Curve (AUC): this is the area of the curve under the Receiver operating characteristic (ROC) curve, which demonstrate the specificity-sensitivity trade-off (TPR vs TNR)

Confusion Matrix

Alternative Metric: Cost Matrix

Sometimes, in the realm of health sciences, we want to punish or reward the model for doing something really well compared to other. For example, in cancer prediction, more emphasis is implaced on avoiding False Negatives (not detecting the cancer), so that we may wish to assign costs/weights (negative means reward) to TP, FP, TN, and FN like this:

This can be implemented as other alternative to above metrics during cross validation. Or, cost matrix can be used to classify one particular record. That is, we can use cost matrix to evaluate risk

With a RandomForest, I am able to extract the probability of a sample as showing SAS or not, then say:

P(SAS) = 0.2
P(neutral, other) = 0.8

Given the above cost matrix, then when I:

• Classify this sample as having SAS, there's a 0.8 probability I am wrong, making a FP error with cost 1, or a risk of 0.8
• Classify this sample as not having SAS, there's a 0.2 probability I am wrong, making a FN error with cost 100, or a risk of 20
• I can then decide to classify this sample based on probability or this new risk factor

load_train = np.load('./data/trainNew.npz', allow_pickle=True)
load_test = np.load('./data/test.npz', allow_pickle=True)
X_train, y_train = load_train['X_train'], load_train['y_train']
X_test, y_test = load_test['X_test'], load_test['y_test']

samples, rows, cols = X_train.shape
print("Before flattening:")
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)

Before flattening:
(9762, 160, 2)
(9762, 2)
(736, 160, 2)
(736, 2)

y_train[:20]

array([[1, 'Yes'],
       [2, 'Yes'],
       [3, 'No'],
       [4, 'Yes'],
       [5, 'Yes'],
       [6, 'Yes'],
       [7, 'No'],
       [9, 'Yes'],
       [10, 'No'],
       [11, 'Yes'],
       [12, 'No'],
       [13, 'No'],
       [14, 'No'],
       [15, 'No'],
       [17, 'Yes'],
       [18, 'Yes'],
       [19, 'No'],
       [20, 'No'],
       [21, 'Yes'],
       [22, 'Yes']], dtype=object)

"""
fst should range anywhere between 0 and 1 in practice, 
and mean squared error is in the same bounds. 

But whether we want equal sized bins or not is still not clear
(we may want higher granularity for bins with high fst and good fit, 
and low for low fst regardless of fit)
"""

# check ranges for f_st
print("Min Fst", np.amin(X_train[:, :, 0]))
print("Max Fst", np.amax(X_train[:, :, 0]))
assert np.amin(X_train[:, :, 0]) >= 0 and np.amax(X_train[:, :, 0]) <= 10

# check ranges for means squared error
print("Min MSE", np.amin(X_train[:, :, 1]))
print("Max MSE", np.amax(X_train[:, :, 1]))
assert np.amin(X_train[:, :, 1]) >= 0 and np.amax(X_train[:, :, 1]) <= 10

# 3. the mean squared error should almost always be smaller than the maximum Fst 
for i in range(0, X_train.shape[0]):
    sample = X_train[i, :]
    minFst, maxFst = min(sample[:, 0]), max(sample[:, 1])
    minMSE, maxMSE = min(sample[:, 1]), max(sample[:, 1])
    assert sum(sample[:, 1] <= maxMSE) == sample.shape[0]

Min Fst 0.0
Max Fst 1.0
Min MSE 0.0
Max MSE 0.999472950447228

sns.histplot(X_train[:, :, 0].reshape(-1), binwidth=0.05)
plt.title("Fst Distribution")
plt.show()

sns.histplot(X_train[:, :, 1].reshape(-1), binwidth=0.05)
plt.title("Mean Squared Error Distribution; No Limit")
# plt.xlim([0, 1])
plt.show()

# None of metrics have MSE >= 1, 
# ~.37% of metrics have MSE >= 0.6
# ~1.532% of metrics have MSE >= 0.4
total = X_train.shape[0] * X_train.shape[1]
for threshold in [1, 0.6, 0.4]:
    errors = sum(X_train[:, :, 1].reshape(-1) >= threshold)
    print(errors, "out of", total, "have MSE >=", threshold, "\t\t", round((errors/total) * 100, 3))

0 out of 1561920 have MSE >= 1 		 0.0
5776 out of 1561920 have MSE >= 0.6 		 0.37
23930 out of 1561920 have MSE >= 0.4 		 1.532

Fst_bins = list(i / 100 for i in range(0, 51, 10))
# Fst_bins = [0, 0.4]
Fst_bins.append(np.amax(X_train[:, :, 0]) + 0.01)
print("Fst bins", Fst_bins)
MSE_bins = list(i / 100 for i in range(0, 51, 10))
# MSE_bins = [0, 0.1, 0.4]
MSE_bins.append(np.amax(X_train[:, :, 1]) + 0.01)
print("MSE bins", MSE_bins)

def makeLabels(iterable):
    # takes an iterable of bin (start, stop) and make labels
    return ["-".join([str(start), str(round(stop, 2))]) for start, stop in zip(iterable, iterable[1:]) ] 
Fst_labels = makeLabels(Fst_bins)
MSE_labels = makeLabels(MSE_bins)

print("Fst labels", Fst_labels)
print("MSE labels", MSE_labels)

def assignBins(Fst_bins, MSE_bins, X_train, X_test):
    Fst_assignments = np.digitize(X_train[:, :, 0], Fst_bins)
    Fst_bins_count = len(Fst_bins)

    MSE_assignments = np.digitize(X_train[:, :, 1], MSE_bins)
    MSE_bins_count = len(MSE_bins)

    # rows: Fst bin #, cols: MSE bin 
    X_train_bins = np.zeros((Fst_bins_count-1, MSE_bins_count-1), dtype=np.int)
    X_test_bins = np.zeros((Fst_bins_count-1, MSE_bins_count-1), dtype=np.int)
    
    def computeMatrix(base, matrix):
        for i in range(0, len(matrix)):
            sample = matrix[i]
            rows = sample.shape[0]

            # transform this (160, 2) matrix into a fxm bin matrix
            empty = np.zeros((Fst_bins_count-1, MSE_bins_count-1), dtype=np.int)
            for j in range(0, rows):
                Fst_bin = Fst_assignments[i, j]
                MSE_bin = MSE_assignments[i, j]
                empty[Fst_bin-1, MSE_bin-1] += 1
            base = np.concatenate((base, empty))

        base = base.reshape(-1, Fst_bins_count-1, MSE_bins_count-1)
        # drop the first entry (only used for appending)
        base = base[1:, :, :]

        return base
    
    X_train_bins = computeMatrix(X_train_bins, X_train)
    X_test_bins = computeMatrix(X_test_bins, X_test)

    return X_train_bins, X_test_bins

X_train_bins, X_test_bins = assignBins(Fst_bins, MSE_bins, X_train, X_test)
print(X_train_bins.shape)
print(X_test_bins.shape)

Fst bins [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 1.01]
MSE bins [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 1.009472950447228]
Fst labels ['0.0-0.1', '0.1-0.2', '0.2-0.3', '0.3-0.4', '0.4-0.5', '0.5-1.01']
MSE labels ['0.0-0.1', '0.1-0.2', '0.2-0.3', '0.3-0.4', '0.4-0.5', '0.5-1.01']
(9762, 6, 6)
(736, 6, 6)

def generateHeatmaps(matrices, Fst_labels, MSE_labels, SAS_labels, savePath):
    for i in range(0, matrices.shape[0]):
        ax = sns.heatmap(
            pd.DataFrame(
                matrices[i, :, :], 
                index=Fst_labels, 
                columns=MSE_labels
            ), 
        linewidths=.5)
        # invert y axis to sort Fst ascending
        ax.invert_yaxis()
        figure = plt.gcf()
        figure.set_size_inches(16, 12)
        plt.title("Output " + str(SAS_labels[i, 0]) + " - " + SAS_labels[i, 1] + " SNS")
        plt.ylabel("Fst")
        plt.xlabel("MSE")

        # save plot
        saveFile = os.path.join(savePath, "output"+str(i+1)+".png")
        plt.savefig(saveFile)
        plt.close() # do NOT show in-line

generateHeatmaps(
    # just do the first 10 to save some time
    X_train_bins[:20], 
    Fst_labels, MSE_labels, y_train, savePath="./Images")

y_train = y_train[:, 1].reshape(y_train.shape[0], )
y_test = y_test[:, 1].reshape(y_test.shape[0], )
# X_train = X_train.reshape(X_train.shape[0], -1)
X_train_bins = X_train_bins.reshape(X_train_bins.shape[0], -1)
X_test_bins = X_test_bins.reshape(X_test.shape[0], -1)

print("After flattening:")
# print(X_train.shape)
print(X_train_bins.shape)
print(y_train.shape)
print(X_test_bins.shape)
print(y_test.shape)

After flattening:
(9762, 36)
(9762,)
(736, 36)
(736,)

# Prepare for K fold cross validation
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score, f1_score
from sklearn.metrics import precision_score, recall_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score

k = 10
# again, random_state for sake of reproducibility
kf = KFold(n_splits=k, random_state = 42, shuffle=True)

# default confusion matrix 
# we can calculate TP, FP, FN, TN from this matrix at the end
matrix = pd.DataFrame(0, index=['True Yes', 'True No', ''], 
                      columns=['Pred Yes', 'Pred No', ''])

start = time.time()
for train_index, test_index in kf.split(X_train_bins):
    # identify the train and test set within each fold
    X_fold_train, X_fold_test = X_train_bins[train_index], X_train_bins[test_index]
    y_fold_train, y_fold_test = y_train[train_index], y_train[test_index]
    
    # fit the model on the training set
#     model = RandomForestClassifier(n_estimators=500, oob_score=True, 
#                                min_samples_leaf = 10, max_depth = 3, 
#                                min_samples_split = 100, max_leaf_nodes = 50,
#                                max_features = 'sqrt', n_jobs=8)

    model = RandomForestClassifier(n_estimators=500, oob_score=True,
                                   max_features = 'sqrt', n_jobs=8)
    model.fit(X_fold_train, y_fold_train)

    # predict label on validation test set, record results
    y_pred = model.predict(X_fold_test)
    y_prob = model.predict_proba(X_fold_test)[:,1]

    # collect metrics
    m = pd.crosstab(y_pred, y_fold_test, margins=True)
    m.index = ['True Yes', 'True No', '']
    m.columns = ['Pred Yes', 'Pred No', '']
    matrix += m

end = time.time()
print('Time taken:', end - start)
print(matrix)
TP, FN = matrix.iloc[0, 0], matrix.iloc[0, 1]
FP, TN = matrix.iloc[1, 0], matrix.iloc[1, 1]
total = TP + FN + FP + TN
print("Accuracy: \t\t\t\t\t\t\t", (TP + TN) / total)
print("Error Rate: \t\t\t\t\t\t\t", (FN + FP) / total)
recall = TP / (TP + FN)
print("True Positive Rate (TPR) | Sensitivity | Recall | Coverage\t", recall)
print("True Negative Rate (TNR) | Specificity \t\t\t\t", 
      TN / (FP + TN))
print("False Positive Rate (FPR) is \t\t\t\t\t", FP / (FP + TN))
print("False Negative Rate (FNR) is \t\t\t\t\t", FN / (TP + FN))
precision = TP / (TP + FP)
print("Average Precision: \t\t\t\t\t\t", precision)
print("Average Recall: \t\t\t\t\t\t", recall)
print("Average F Measures: \t\t\t\t\t\t", 
      (2*precision*recall) / (precision+recall))

Time taken: 10.884536266326904
          Pred Yes  Pred No      
True Yes      2626     2153  4779
True No       2223     2760  4983
              4849     4913  9762
Accuracy: 							 0.5517312026224135
Error Rate: 							 0.44826879737758657
True Positive Rate (TPR) | Sensitivity | Recall | Coverage	 0.5494873404477925
True Negative Rate (TNR) | Specificity 				 0.5538832028898254
False Positive Rate (FPR) is 					 0.4461167971101746
False Negative Rate (FNR) is 					 0.4505126595522076
Average Precision: 						 0.5415549597855228
Average Recall: 						 0.5494873404477925
Average F Measures: 						 0.5454923140839218

# https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
# n_estimators = number of trees to bag
# Fit the model on all training samples possible
# clf = RandomForestClassifier(n_estimators=500, criterion='gini', max_depth=None, 
#                         min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, 
#                         max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, 
#                         min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=None, 
#                         random_state=None, verbose=0, warm_start=False, class_weight=None, 
#                         ccp_alpha=0.0, max_samples=None)
# Tune: min_samples_split, max_leaf_nodes, max_depth and min_samples_leaf.
model = RandomForestClassifier(n_estimators=500, oob_score=True,
                               max_features = 'sqrt', n_jobs=8)
# model = RandomForestClassifier(n_estimators=500, oob_score=True, 
#                                min_samples_leaf = 10, max_depth = 5, 
#                                min_samples_split = 100, max_leaf_nodes = 50,
#                                max_features = 'sqrt', n_jobs=8)
start = time.time()
model.fit(X_train_bins, y_train)
end = time.time()
print('Time taken:', end - start)

# save the model
# joblib.dump(model, "./data/random_forest_10000.joblib")

from itertools import product
feature_labels = [ 
    "Fst " + i + "; MSE " + j for i, j in product(Fst_labels, MSE_labels) 
]

for f, imp in zip(feature_labels, model.feature_importances_):
    print(f'Feature {f} importance: {imp} \t\t\t\t\t\t')

# take the top 20 most important features
feature_imp = pd.Series(
    model.feature_importances_, index=feature_labels
).sort_values(ascending=False)[:20]

%matplotlib inline
# Creating a bar plot
sns.barplot(x=feature_imp, y=feature_imp.index)
plt.xlabel('Feature Importance Score')
plt.title("Top 20 Important Features In Detecting SAS")
plt.show()

Time taken: 1.0431461334228516
Feature Fst 0.0-0.1; MSE 0.0-0.1 importance: 0.0710402293844183 						
Feature Fst 0.0-0.1; MSE 0.1-0.2 importance: 0.0 						
Feature Fst 0.0-0.1; MSE 0.2-0.3 importance: 0.0 						
Feature Fst 0.0-0.1; MSE 0.3-0.4 importance: 0.0 						
Feature Fst 0.0-0.1; MSE 0.4-0.5 importance: 0.0 						
Feature Fst 0.0-0.1; MSE 0.5-1.01 importance: 0.0 						
Feature Fst 0.1-0.2; MSE 0.0-0.1 importance: 0.13522019289565124 						
Feature Fst 0.1-0.2; MSE 0.1-0.2 importance: 0.0 						
Feature Fst 0.1-0.2; MSE 0.2-0.3 importance: 0.0 						
Feature Fst 0.1-0.2; MSE 0.3-0.4 importance: 0.0 						
Feature Fst 0.1-0.2; MSE 0.4-0.5 importance: 0.0 						
Feature Fst 0.1-0.2; MSE 0.5-1.01 importance: 0.0 						
Feature Fst 0.2-0.3; MSE 0.0-0.1 importance: 0.1345050139760378 						
Feature Fst 0.2-0.3; MSE 0.1-0.2 importance: 0.012893591107673338 						
Feature Fst 0.2-0.3; MSE 0.2-0.3 importance: 0.0 						
Feature Fst 0.2-0.3; MSE 0.3-0.4 importance: 0.0 						
Feature Fst 0.2-0.3; MSE 0.4-0.5 importance: 0.0 						
Feature Fst 0.2-0.3; MSE 0.5-1.01 importance: 0.0 						
Feature Fst 0.3-0.4; MSE 0.0-0.1 importance: 0.11377733730327241 						
Feature Fst 0.3-0.4; MSE 0.1-0.2 importance: 0.09385347240263789 						
Feature Fst 0.3-0.4; MSE 0.2-0.3 importance: 0.0004940507601552831 						
Feature Fst 0.3-0.4; MSE 0.3-0.4 importance: 0.0 						
Feature Fst 0.3-0.4; MSE 0.4-0.5 importance: 0.0 						
Feature Fst 0.3-0.4; MSE 0.5-1.01 importance: 0.0 						
Feature Fst 0.4-0.5; MSE 0.0-0.1 importance: 0.03458605240891403 						
Feature Fst 0.4-0.5; MSE 0.1-0.2 importance: 0.10506743925589826 						
Feature Fst 0.4-0.5; MSE 0.2-0.3 importance: 0.05973049942868652 						
Feature Fst 0.4-0.5; MSE 0.3-0.4 importance: 0.003821765924582709 						
Feature Fst 0.4-0.5; MSE 0.4-0.5 importance: 0.0 						
Feature Fst 0.4-0.5; MSE 0.5-1.01 importance: 0.0 						
Feature Fst 0.5-1.01; MSE 0.0-0.1 importance: 0.0013785182321146734 						
Feature Fst 0.5-1.01; MSE 0.1-0.2 importance: 0.03538303239164782 						
Feature Fst 0.5-1.01; MSE 0.2-0.3 importance: 0.0682474996564863 						
Feature Fst 0.5-1.01; MSE 0.3-0.4 importance: 0.05634904912908015 						
Feature Fst 0.5-1.01; MSE 0.4-0.5 importance: 0.03768850041601347 						
Feature Fst 0.5-1.01; MSE 0.5-1.01 importance: 0.03596375532672975 						

# Extract single tree
estimator = model.estimators_[203]

from sklearn.tree import export_graphviz
# Export as dot file
export_graphviz(estimator, out_file='tree.dot', 
                feature_names = feature_labels,
                class_names = ["SAS", "Neutral"],
                rounded = True, proportion = False, 
                precision = 2, filled = True)

# Convert to png using system command (requires Graphviz)
from subprocess import call
call(['dot', '-Tpng', 'tree.dot', '-o', 'tree.png', '-Gdpi=600'])

# Display in jupyter notebook
from IPython.display import Image
display(Image(filename = 'tree.png'))

from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import MinMaxScaler

param_grid = {
    'max_depth':list(range(45,55)),
    'min_samples_leaf':list(range(10,50,5)),
    'max_features':['sqrt', 'log2'],
    'criterion':['entropy', 'gini']
}

clf = GridSearchCV(RandomForestClassifier(), param_grid, 
                   scoring="accuracy", cv=5, n_jobs=8)

rf_pred = cross_val_predict(
    clf, 
    X=X_train_bins, y=y_train, cv=5, n_jobs=12)
print("The average nested cross validation accuracy is ", accuracy_score(y_train, rf_pred))

print(confusion_matrix(y_train, rf_pred))

print(classification_report(y_train, rf_pred))

clf.fit(X_train_bins, y_train)
print(clf.best_params_)

The average nested cross validation accuracy is  0.5422044662978898
[[2369 2480]
 [1989 2924]]
              precision    recall  f1-score   support

          No       0.54      0.49      0.51      4849
         Yes       0.54      0.60      0.57      4913

    accuracy                           0.54      9762
   macro avg       0.54      0.54      0.54      9762
weighted avg       0.54      0.54      0.54      9762

{'criterion': 'gini', 'max_depth': 51, 'max_features': 'log2', 'min_samples_leaf': 10}

Test Set Evaluation

Below are peformance measures on the test set never touched during model building

y_pred = model.predict(X_test_bins)
print("Accuracy on Test Set:", accuracy_score(y_test,y_pred))
print(f'Our OOB prediction of accuracy is: {model.oob_score_ * 100}%')

# show stats
matrix = pd.crosstab(y_pred, y_test, margins=True)
matrix.index = ['True Yes', 'True No', '']
matrix.columns = ['Pred Yes', 'Pred No', '']
print(matrix)

TP, FN = matrix.iloc[0, 0], matrix.iloc[0, 1]
FP, TN = matrix.iloc[1, 0], matrix.iloc[1, 1]
total = TP + FN + FP + TN
print("Accuracy: \t\t\t\t\t\t\t", (TP + TN) / total)
print("Error Rate: \t\t\t\t\t\t\t", (FN + FP) / total)
print("True Positive Rate (TPR) | Sensitivity | Recall | Coverage\t", 
      TP / (TP + FN))
print("True Negative Rate (TNR) | Specificity \t\t\t\t", 
      TN / (FP + TN))
print("False Positive Rate (FPR) is \t\t\t\t\t", FP / (FP + TN))
print("False Negative Rate (FNR) is \t\t\t\t\t", FN / (TP + FN))
precision = TP / (TP + FP)
recall = TP / (TP + FN)
print("Average Precision: \t\t\t\t\t\t", precision)
print("Average F Measures: \t\t\t\t\t\t", 
      (2*recall*precision) / (recall + precision))
pred_prob = model.predict_proba(X_test_bins)[:, 1]
print("Average AUC: \t\t\t\t\t\t\t", roc_auc_score(y_test, pred_prob))

Accuracy on Test Set: 0.46875
Our OOB prediction of accuracy is: 55.38824011473059%
          Pred Yes  Pred No     
True Yes       155      210  365
True No        181      190  371
               336      400  736
Accuracy: 							 0.46875
Error Rate: 							 0.53125
True Positive Rate (TPR) | Sensitivity | Recall | Coverage	 0.4246575342465753
True Negative Rate (TNR) | Specificity 				 0.5121293800539084
False Positive Rate (FPR) is 					 0.48787061994609165
False Negative Rate (FNR) is 					 0.5753424657534246
Average Precision: 						 0.46130952380952384
Average F Measures: 						 0.4422253922967189
Average AUC: 							 0.47805803571428573

from sklearn.metrics import precision_recall_curve

# precision recall curve for training set 
y_prob = model.predict_proba(X_test_bins)[:,1]
precisions, recalls, thresholds = precision_recall_curve(
    y_test, y_prob, pos_label = "Yes")

def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):
    plt.plot(thresholds, precisions[:-1], "b--", label="Precision")
    plt.plot(thresholds, recalls[:-1], "g-", label="Recall")
    plt.xlabel("Threshold")
    plt.legend(loc="upper left")
    plt.ylim([0, 1])

plot_precision_recall_vs_threshold(precisions, recalls, thresholds)
plt.show()

plt.plot(precisions, recalls, "b--", label="Precision")
plt.xlabel("Precision")
plt.ylabel("Recall")
plt.legend(loc="upper left")
plt.show()

# ROC Curve
# The more the area under the curve the better our classifier
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_test, y_prob, pos_label = "Yes")
def plot_roc_curve(fpr, tpr, label=None):
    plt.plot(fpr, tpr, linewidth=2, label=label)
    plt.plot([0, 1], [0, 1], 'k--')
    plt.axis([0, 1, 0, 1])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')

plot_roc_curve(fpr, tpr, "Random Forest")
plt.show()

from sklearn.metrics import roc_auc_score
print("The AUC is ", roc_auc_score(y_test, y_prob))

The AUC is  0.47805803571428573

from IPython.display import Image
for i in range(1, 11):
    path = "./Images/output" + str(i) + ".png"
    display(Image(filename=path))

Issues

Can try a variety of bin values, but all results in low cross validated and test accuracy...

Reason: assumption that SAS samples will have 'same regions that light up' not exactly true;

From the heatmaps above, SAS samples do NOT seem much different compared to Non-SAS samples